Nutrient estimation from an FFQ developed for a black Zimbabwean population

There is little information in the literature on methods of food composition database development to calculate nutrient intake from food frequency questionnaire (FFQ) data. The aim of this study is to describe the development of an FFQ and a food composition table to calculate nutrient intake in a B...

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Veröffentlicht in:Nutrition journal Jg. 4; H. 1; S. 37
Hauptverfasser: Merchant, Anwar T, Dehghan, Mahshid, Chifamba, Jephat, Terera, Getrude, Yusuf, Salim
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England BioMed Central 13.12.2005
BMC
Schlagworte:
Adult
Aged
Aged, 80 and over
Black or African American
Black People
Databases as Topic
Diet
Diet Records
Dietary Carbohydrates - administration & dosage
Energy Intake
Female
Food
Humans
Income
Male
Middle Aged
Nutritional Physiological Phenomena
Nutritive Value
Rural Population
Seasons
Surveys and Questionnaires
United States
United States Department of Agriculture
Urban Population
Zea mays
Zimbabwe
United States
Zimbabwe
ISSN:1475-2891, 1475-2891
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Abstract There is little information in the literature on methods of food composition database development to calculate nutrient intake from food frequency questionnaire (FFQ) data. The aim of this study is to describe the development of an FFQ and a food composition table to calculate nutrient intake in a Black Zimbabwean population. Trained interviewers collected 24-hour dietary recalls (24 hr DR) from high and low income families in urban and rural Zimbabwe. Based on these data and input from local experts we developed an FFQ, containing a list of frequently consumed foods, standard portion sizes, and categories of consumption frequency. We created a food composition table of the foods found in the FFQ so that we could compute nutrient intake. We used the USDA nutrient database as the main resource because it is relatively complete, updated, and easily accessible. To choose the food item in the USDA nutrient database that most closely matched the nutrient content of the local food we referred to a local food composition table. Almost all the participants ate sadza (maize porridge) at least 5 times a week, and about half had matemba (fish) and caterpillar more than once a month. Nutrient estimates obtained from the FFQ data by using the USDA and Zimbabwean food composition tables were similar for total energy intake intra class correlation (ICC) = 0.99, and carbohydrate (ICC = 0.99), but different for vitamin A (ICC = 0.53), and total folate (ICC = 0.68). We have described a standardized process of FFQ and food composition database development for a Black Zimbabwean population.
AbstractList Abstract Background There is little information in the literature on methods of food composition database development to calculate nutrient intake from food frequency questionnaire (FFQ) data. The aim of this study is to describe the development of an FFQ and a food composition table to calculate nutrient intake in a Black Zimbabwean population. Methods Trained interviewers collected 24-hour dietary recalls (24 hr DR) from high and low income families in urban and rural Zimbabwe. Based on these data and input from local experts we developed an FFQ, containing a list of frequently consumed foods, standard portion sizes, and categories of consumption frequency. We created a food composition table of the foods found in the FFQ so that we could compute nutrient intake. We used the USDA nutrient database as the main resource because it is relatively complete, updated, and easily accessible. To choose the food item in the USDA nutrient database that most closely matched the nutrient content of the local food we referred to a local food composition table. Results Almost all the participants ate sadza (maize porridge) at least 5 times a week, and about half had matemba (fish) and caterpillar more than once a month. Nutrient estimates obtained from the FFQ data by using the USDA and Zimbabwean food composition tables were similar for total energy intake intra class correlation (ICC) = 0.99, and carbohydrate (ICC = 0.99), but different for vitamin A (ICC = 0.53), and total folate (ICC = 0.68). Conclusion We have described a standardized process of FFQ and food composition database development for a Black Zimbabwean population.
There is little information in the literature on methods of food composition database development to calculate nutrient intake from food frequency questionnaire (FFQ) data. The aim of this study is to describe the development of an FFQ and a food composition table to calculate nutrient intake in a Black Zimbabwean population. Trained interviewers collected 24-hour dietary recalls (24 hr DR) from high and low income families in urban and rural Zimbabwe. Based on these data and input from local experts we developed an FFQ, containing a list of frequently consumed foods, standard portion sizes, and categories of consumption frequency. We created a food composition table of the foods found in the FFQ so that we could compute nutrient intake. We used the USDA nutrient database as the main resource because it is relatively complete, updated, and easily accessible. To choose the food item in the USDA nutrient database that most closely matched the nutrient content of the local food we referred to a local food composition table. Almost all the participants ate sadza (maize porridge) at least 5 times a week, and about half had matemba (fish) and caterpillar more than once a month. Nutrient estimates obtained from the FFQ data by using the USDA and Zimbabwean food composition tables were similar for total energy intake intra class correlation (ICC) = 0.99, and carbohydrate (ICC = 0.99), but different for vitamin A (ICC = 0.53), and total folate (ICC = 0.68). We have described a standardized process of FFQ and food composition database development for a Black Zimbabwean population.
There is little information in the literature on methods of food composition database development to calculate nutrient intake from food frequency questionnaire (FFQ) data. The aim of this study is to describe the development of an FFQ and a food composition table to calculate nutrient intake in a Black Zimbabwean population.BACKGROUNDThere is little information in the literature on methods of food composition database development to calculate nutrient intake from food frequency questionnaire (FFQ) data. The aim of this study is to describe the development of an FFQ and a food composition table to calculate nutrient intake in a Black Zimbabwean population.Trained interviewers collected 24-hour dietary recalls (24 hr DR) from high and low income families in urban and rural Zimbabwe. Based on these data and input from local experts we developed an FFQ, containing a list of frequently consumed foods, standard portion sizes, and categories of consumption frequency. We created a food composition table of the foods found in the FFQ so that we could compute nutrient intake. We used the USDA nutrient database as the main resource because it is relatively complete, updated, and easily accessible. To choose the food item in the USDA nutrient database that most closely matched the nutrient content of the local food we referred to a local food composition table.METHODSTrained interviewers collected 24-hour dietary recalls (24 hr DR) from high and low income families in urban and rural Zimbabwe. Based on these data and input from local experts we developed an FFQ, containing a list of frequently consumed foods, standard portion sizes, and categories of consumption frequency. We created a food composition table of the foods found in the FFQ so that we could compute nutrient intake. We used the USDA nutrient database as the main resource because it is relatively complete, updated, and easily accessible. To choose the food item in the USDA nutrient database that most closely matched the nutrient content of the local food we referred to a local food composition table.Almost all the participants ate sadza (maize porridge) at least 5 times a week, and about half had matemba (fish) and caterpillar more than once a month. Nutrient estimates obtained from the FFQ data by using the USDA and Zimbabwean food composition tables were similar for total energy intake intra class correlation (ICC) = 0.99, and carbohydrate (ICC = 0.99), but different for vitamin A (ICC = 0.53), and total folate (ICC = 0.68).RESULTSAlmost all the participants ate sadza (maize porridge) at least 5 times a week, and about half had matemba (fish) and caterpillar more than once a month. Nutrient estimates obtained from the FFQ data by using the USDA and Zimbabwean food composition tables were similar for total energy intake intra class correlation (ICC) = 0.99, and carbohydrate (ICC = 0.99), but different for vitamin A (ICC = 0.53), and total folate (ICC = 0.68).We have described a standardized process of FFQ and food composition database development for a Black Zimbabwean population.CONCLUSIONWe have described a standardized process of FFQ and food composition database development for a Black Zimbabwean population.
ArticleNumber 37
Author Merchant, Anwar T
Chifamba, Jephat
Dehghan, Mahshid
Terera, Getrude
Yusuf, Salim
AuthorAffiliation 4 Department of Physiology, University of Zimbabwe College of Health Sciences, Harare, Zimbabwe
3 Department of Medicine, McMaster University, Hamilton ON, Canada
1 Population health Research Institute, McMaster University, Hamilton ON, Canada
2 Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton ON, Canada
AuthorAffiliation_xml – name: 1 Population health Research Institute, McMaster University, Hamilton ON, Canada
– name: 4 Department of Physiology, University of Zimbabwe College of Health Sciences, Harare, Zimbabwe
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Cites_doi 10.1093/acprof:oso/9780195122978.003.05
10.1093/oxfordjournals.aje.a116211
10.1186/1475-2891-4-18
10.1016/S0305-750X(99)00094-7
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Snippet There is little information in the literature on methods of food composition database development to calculate nutrient intake from food frequency...
Abstract Background There is little information in the literature on methods of food composition database development to calculate nutrient intake from food...
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StartPage 37
SubjectTerms Adult
Aged
Aged, 80 and over
Black or African American
Black People
Databases as Topic
Diet
Diet Records
Dietary Carbohydrates - administration & dosage
Energy Intake
Female
Food
Humans
Income
Male
Middle Aged
Nutritional Physiological Phenomena
Nutritive Value
Rural Population
Seasons
Surveys and Questionnaires
United States
United States Department of Agriculture
Urban Population
Zea mays
Zimbabwe
Title Nutrient estimation from an FFQ developed for a black Zimbabwean population
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